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A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning
Abstract
Contrastive learning enables models to learn graph structural information through self-supervised learning in the absence of labels. However, real-world networks often contain both graph structural information and incomplete node attribute information. Based on this, this paper proposes a heterogeneous network text attribute fusion method based on multi-layer semantic relation contrastive learning. Firstly, the heterogeneous network components are reconstructed using semantic and thematic attribute acquisition methods at different levels, obtaining semantic representations of text attributes at various levels of abstraction. Then, the contrastive learning component of the heterogeneous network is employed to maximize the correlation between different views of the heterogeneous network, allowing the two heterogeneous networks to align in this space. This alignment helps to uncover the latent connections between text attribute features across different views, thereby achieving the fusion of information between views.
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